Joint optimization of level of repair analysis and spare parts stocks.

In the field of service logistics for capital goods, generally, metric type methods are used to decide where to stock spare parts in a multi-echelon repair network such that a target availability of the capital goods is achieved. These methods generate a trade-off curve of spares investment costs versus backorders. Backorders of spare parts lead to unavailability of the capital goods. Inputs in the spare parts stocking problem are decisions on 1) which components to repair upon failure and which to discard, and 2) at which locations in the repair network to perform the repairs and discards. The level of repair analysis (lora) can be used to make such decisions in conjunction with the decisions 3) at which locations to deploy resources, such as test equipment, that may be required to repair, discard, or move components. Since these decisions significantly impact the spare parts investment costs, we propose to solve the lora and spare parts stocking problems jointly. We design an algorithm that finds efficient points, i.e., lower backorder levels cannot be achieved against the same (or lower) costs. In a computational experiment, we show that solving the joint problem is worthwhile, since we achieve a cost reduction of 5.1% on average and over 43% at maximum compared with using a sequential approach of first solving a lora and then the spare parts stocking problem.